In the fast-evolving landscape of AI-assisted programming, developers have grown accustomed to increasingly sophisticated coding agents that promise to revolutionize how we write code. Yet, as these tools accumulate features, some developers are finding themselves yearning for simplicity and predictability. This is the story of one developer's journey to build an alternative that strips away the complexity while maintaining power and effectiveness.

The Evolution of Coding Assistants

Over the past three years, the journey of AI-assisted coding has followed a familiar path for many developers: starting with simple copy-paste interactions with ChatGPT, progressing to tools like GitHub Copilot (which never worked for everyone), then to dedicated environments like Cursor, and finally to the new generation of coding agent harnesses such as Claude Code, Codex, Amp, Droid, and opencode that became daily drivers in 2025.

"I preferred Claude Code for most of my work," writes Mario Zechner, the creator of the 'pi' coding agent. "It was the first thing I tried back in April after using Cursor for a year and a half. Back then, it was much more basic. That fit my workflow perfectly, because I'm a simple boy who likes simple, predictable tools."

However, as these tools evolved, they began to accumulate features that Zechner found unnecessary and disruptive. "Over the past few months, Claude Code has turned into a spaceship with 80% of functionality I have no use for. The system prompt and tools also change on every release, which breaks my workflows and changes model behavior. I hate that. Also, it flickers."

This frustration led Zechner to embark on an ambitious project: building his own coding agent harness from scratch, with a philosophy of minimalism and predictability at its core.

The Architecture of Simplicity

The 'pi' project comprises four main components:

  1. pi-ai: A unified LLM API supporting multiple providers (Anthropic, OpenAI, Google, xAI, Groq, Cerebras, OpenRouter, and any OpenAI-compatible endpoint)
  2. pi-agent-core: An agent loop handling tool execution, validation, and event streaming
  3. pi-tui: A minimal terminal UI framework with differential rendering
  4. pi-coding-agent: The CLI that integrates all components with session management, custom tools, themes, and project context

"My philosophy in all of this was: if I don't need it, it won't be built. And I don't need a lot of things," Zechner explains.

The Challenge of Provider Abstraction

Creating a unified LLM API across multiple providers presents significant technical challenges. "There's really only four APIs you need to speak to talk to pretty much any LLM provider: OpenAI's Completions API, their newer Responses API, Anthropic's Messages API, and Google's Generative AI API," Zechner notes.

However, each provider implements these APIs with subtle but important differences. "For example, in openai-completions.ts: Cerebras, xAI, Mistral, and Chutes don't like the store field; Mistral and Chutes use max_tokens instead of max_completion_tokens; Cerebras, xAI, Mistral, and Chutes don't support the developer role for system prompts; Grok models don't like reasoning_effort; Different providers return reasoning content in different fields (reasoning_content vs reasoning)."

To address these inconsistencies, pi-ai implements extensive testing across all supported providers and models. "While this is a good effort, it still won't guarantee that new models and providers will just work out of the box," Zechner acknowledges.

Context Handoff: The Multi-Model World

One of pi-ai's most distinctive features is its ability to maintain context across different providers. "Context handoff between providers was a feature pi-ai was designed for from the start. Since each provider has their own way of tracking tool calls and thinking traces, this can only be a best-effort thing," Zechner explains.

When switching from one provider to another mid-session, the system transforms context appropriately. "For example, if you switch from Anthropic to OpenAI mid-session, Anthropic thinking traces are converted to content blocks inside assistant messages, delimited by tags."

This approach enables developers to start a conversation with one model and continue with another, potentially leveraging the strengths of different models for different parts of a task.

The Terminal UI: Differential Rendering

For the user interface, Zechner chose a terminal-based approach, inspired by his experience with early computing environments. "I grew up in the DOS era, so terminal user interfaces are what I grew up with. From the fancy setup programs for Doom to Borland products, TUIs were with me until the end of the 90s."

After evaluating existing TUI frameworks like Ink, Blessed, and OpenTUI, Zechner decided to build his own. "I definitely don't want to write my TUI like a React app. Blessed seems to be mostly unmaintained, and OpenTUI is explicitly not production ready. Also, writing my own TUI framework on top of Node.js seemed like a fun little challenge."

pi-tui implements a "retained mode" approach where components persist across frames, caching their output when unchanged. The key innovation is "differential rendering," which only redraws what has changed since the last render.

"The algorithm is simple: First render - Just output all lines to the terminal; Width changed - Clear screen completely and re-render everything; Normal update - Find the first line that differs from what's on screen, move the cursor to that line, and re-render from there to the end," Zechner describes.

To prevent flicker, the framework uses synchronized output escape sequences that tell the terminal to buffer output and display it atomically. "In any capable terminal like Ghostty or iTerm2, this works brilliantly and you never see any flicker. In less fortunate terminal implementations like VS Code's built-in terminal, you will get some flicker," Zechner notes.

The Philosophy of Minimalism

Where pi truly distinguishes itself is in its deliberate design choices that buck current trends in AI-assisted development tools.

Minimal System Prompt: Unlike competitors that use extensive system prompts (often 10,000+ tokens), pi uses a concise prompt under 1000 tokens that focuses on the essentials:

You are an expert coding assistant. You help users with coding tasks by reading files, executing commands, editing code, and writing new files.

Available tools:
- read: Read file contents
- bash: Execute bash commands
- edit: Make surgical edits to files
- write: Create or overwrite files

Guidelines:
- Use bash for file operations like ls, grep, find
- Use read to examine files before editing
- Use edit for precise changes (old text must match exactly)
- Use write only for new files or complete rewrites
- When summarizing your actions, output plain text directly - do NOT use cat or bash to display what you did
- Be concise in your responses
- Show file paths clearly when working with files

Minimal Toolset: pi provides only four core tools (read, write, edit, bash), with additional read-only tools available optionally. "As it turns out, these four tools are all you need for an effective coding agent. Models know how to use bash and have been trained on the read, write, and edit tools with similar input schemas," Zechner argues.

YOLO by Default: pi operates without safety rails or permission prompts, assuming users know what they're doing. "pi runs in full YOLO mode and assumes you know what you're doing. It has unrestricted access to your filesystem and can execute any command without permission checks or safety rails," Zechner states.

He defends this approach by pointing out that security measures in other tools are largely "security theater." "As soon as your agent can write code and run code, it's pretty much game over. The only way you could prevent exfiltration of data would be to cut off all network access for the execution environment the agent runs in, which makes the agent mostly useless."

No Built-in To-Dos: pi deliberately avoids task management features. "pi does not and will not support built-in to-dos. In my experience, to-do lists generally confuse models more than they help. They add state that the model has to track and update, which introduces more opportunities for things to go wrong."

Instead, Zechner advocates for external task tracking through simple files like TODO.md that the agent can read and update as needed.

No Plan Mode: Similarly, pi lacks dedicated planning functionality. "pi does not and will not have a built-in plan mode. Telling the agent to think through a problem together with you, without modifying files or executing commands, is generally sufficient."

No MCP Support: Model Context Protocol (MCP) integration is another feature conspicuously absent from pi. "pi does not and will not support MCP. I've written about this extensively, but the TL;DR is: MCP servers are overkill for most use cases, and they come with significant context overhead."

Zechner argues that MCP servers like Playwright MCP (21 tools, 13.7k tokens) or Chrome DevTools MCP (26 tools, 18k tokens) consume too much context space. "That's 7-9% of your context window gone before you even start working. Many of these tools you'll never use in a given session."

No Background Bash: pi's bash tool runs commands synchronously, without background process management. "pi's bash tool runs commands synchronously. There's no built-in way to start a dev server, run tests in the background, or interact with a REPL while the command is still running."

Instead, Zechner recommends using tmux for background processes, which provides better observability. "Use tmux instead. How's that for observability? The same approach works for long-running dev servers, watching log output, and similar use cases."

No Sub-agents: pi avoids the pattern of spawning sub-agents for complex tasks, which is common in other tools. "pi does not have a dedicated sub-agent tool. When Claude Code needs to do something complex, it often spawns a sub-agent to handle part of the task. You have zero visibility into what that sub-agent does."

Zechner believes this lack of observability makes debugging difficult. "If the sub-agent makes a mistake, debugging is painful because you can't see the full conversation."

Instead, he suggests using bash to spawn new pi instances when needed, maintaining full observability over the entire process.

Benchmarks: Proof in the Pudding

Zechner's contrarian approach raises an important question: does minimalism come at the cost of performance? To answer this, he conducted benchmarks comparing pi against other coding agents using Terminal-Bench 2.0.

The results show pi performing competitively against more complex tools. "I performed a complete run with five trials per task, which makes the results eligible for submission to the leaderboard," Zechner explains.

On the Terminal-Bench leaderboard as of December 2nd, 2025, pi holds a strong position, demonstrating that its minimalist approach doesn't compromise on effectiveness.

Interestingly, Zechner also notes the performance of Terminus 2, a minimal agent that provides only a tmux session for the model to interact with directly. "Terminus 2 is the Terminal-Bench team's own minimal agent that just gives the model a tmux session. The model sends commands as text to tmux and parses the terminal output itself. No fancy tools, no file operations, just raw terminal interaction. And it's holding its own against agents with far more sophisticated tooling."

This result, Zechner suggests, provides "more evidence that a minimal approach can do just as well."

The Future of AI-Assisted Development

The story of pi is more than just another tool in the crowded field of AI-assisted development. It represents a philosophical counterpoint to the trend of adding more features and complexity in the name of capability.

"Benchmark results are hilarious, but the real proof is in the pudding. And my pudding is my day-to-day work, where pi has been performing admirably," Zechner concludes. "Twitter is full of context engineering posts and blogs, but I feel like none of the harnesses we currently have actually let you do context engineering. pi is my attempt to build myself a tool where I'm in control as much as possible."

As AI-assisted development continues to evolve, the lessons from pi's development offer valuable insights: that minimalism can coexist with power, that predictability matters, and that giving users control and observability may be more valuable than adding ever more sophisticated abstractions.

In an industry often captivated by the "more is better" mentality, pi stands as a testament to the enduring value of simplicity and focus. It's not just another coding agent—it's a statement about what AI-assisted development could and should be.